A multispectral processing chain for chlorophyll content assessment in banana fields by UAV imagery
2019
Rabatel, Gilles | Lamour, J. | Moura, Daniel | Naud, O. | Information – Technologies – Analyse Environnementale – Procédés Agricoles (UMR ITAP) ; Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro) | COMPAGNIE FRUITIERE MARSEILLE FRA ; Partenaires IRSTEA ; Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)
[Departement_IRSTEA]Ecotechnologies [TR1_IRSTEA]INSPIRE [ADD1_IRSTEA]Équiper l'agriculture
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Показать больше [+] Меньше [-]Английский. A specific processing chain has been designed to build leaf chlorophyll content (LCC) prediction maps in banana fields using multispectral UAV imagery. A supervised regression model was first calibrated out-of-field using multispectral images of collected leaves laid on the ground, and then applied to build a LCC ortho-mosaic for a complete field using the same multispectral sensor. Though the quality of the resulting LCC map was limited mainly due to light scattering inside the canopy, a significant correlation with within-field vigor heterogeneity has been found, making this approach promising for further banana production management.
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